A new paradigm for data-driven, model-agnostic new physics searches at colliders is
emerging, and aims to leverage recent breakthroughs in anomaly detection and machine …

Interest in deep learning in collider physics has been growing in recent years, specifically in
applying these methods in jet classification, anomaly detection, particle identification etc …

A bstract The presence of charged Higgs bosons is a generic prediction of multiplet
extensions of the Standard Model (SM) Higgs sector. Focusing on the Two-Higgs-Doublet …

We construct a procedure to separate boosted Higgs bosons decaying into hadrons, from
the background due to strong interactions. We employ the Lund jet plane to obtain a …

A bstract Ensemble learning is a technique where multiple component learners are
combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the …

A bstract In this paper, a model inspired by Grand Unification principles featuring three
generations of vector-like fermions, new Higgs doublets and a rich neutrino sector at the low …

In this work, we continue our exploration of TeV-scale vector-like fermion signatures inspired
by a Grand Unification scenario based on the trinification gauge group. A particular focus is …

Experimentally probing the charm-Yukawa coupling in the LHC experiments is important,
but very challenging due to an enormous QCD background. We study a new channel that …

This thesis focuses on exploring the interactions of the Higgs boson with other particles and
their connections to Beyond the Standard Model (BSM) physics at the future High Luminosity …